公开(公告)号：US20240046474A1

公开(公告)日：2024-02-08

申请号：US18487438

申请日：2023-10-16

Applicant: ADIUVO DIAGNOSTICS PRIVATE LIMITED

Inventor： Bala PESALA ,  Geethanjali RADHAKRISHNAN ,  Bikki KUMAR SHA ,  John KING

IPC: G06T7/00 ,  G06T7/50 ,  G06V10/10 ,  G06V10/60 ,  G06V10/82

CPC classification number: G06T7/0012 ,  G06T7/50 ,  G06V10/17 ,  G06V10/60 ,  G06V10/82 ,  G06V2201/03 ,  G06V2201/07 ,  G06T2207/10064 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30024 ,  G06T2207/30088

Abstract: Techniques are for detecting presence of a problematic cellular entity in a target. In an example, using an analysis model, a fluorescence-based image is analyzed. The analysis model is trained using a number of reference fluorescence-based images for detecting the presence of problematic cellular entities in targets. Based on the analysis, a problematic cellular entity present in the target is detected. To perform the detection, the analysis model is trained to differentiate between the fluorescence in the fluorescence-based image emerging from the problematic cellular entity and the fluorescence in the fluorescence-based image emerging from regions other than the problematic cellular entity.

公开(公告)号：US20240041419A1

公开(公告)日：2024-02-08

申请号：US18450341

申请日：2023-08-15

Applicant: Dalhousie University

Inventor： Mike SATTARIVAND ,  Ivan ROMADANOV

IPC: A61B6/00 ,  G01T1/17 ,  G06T5/00 ,  G06T5/50 ,  G06T5/20 ,  G06V10/22 ,  G06T7/00

CPC classification number: A61B6/5258 ,  G01T1/17 ,  A61B6/482 ,  A61B6/583 ,  G06T5/002 ,  G06T5/50 ,  G06T5/20 ,  G06V10/22 ,  G06T7/0012 ,  G06T2207/20224 ,  G06T2207/10116 ,  G06V2201/03 ,  G06T2207/30008

Abstract: Methods for adaptive dual-energy imaging comprise calibrating a fitting model and implementing the calibrated model. Calibrating the model comprises: acquiring high and low energy images of a step phantom, generating regions of interest with overlapping materials, and determining an average intensity for each region of interest in each of the images; and determining a model material cancellation weighting factor and a model noise cancellation weighting factor for each of a first material and a second material for each region of interest. The weighting factors are fit to a fitting model. Implementing the calibrated model comprises: acquiring high and low energy images of a subject and generating maps of a subject-specific material cancellation weighting factor and a subject-specific noise cancellation weighting factor according to the fitting model; and applying the maps of the subject-specific material cancellation weighting factor and the subject-specific noise cancellation weighting factor to the images of the subject.

公开(公告)号：US11894114B2

公开(公告)日：2024-02-06

申请号：US17143808

申请日：2021-01-07

Applicant: Sirona Medical, Inc.

Inventor： David Seungwon Paik ,  Vernon Marshall ,  Mark D. Longo ,  Cameron Andrews ,  Kojo Worai Osei ,  Berk Norman ,  Ankit Goyal

IPC: G06N3/08 ,  G16H15/00 ,  G06T7/11 ,  G06F3/01 ,  G06F3/16 ,  G06N3/04 ,  G10L15/22 ,  G06V10/94 ,  G06F18/214 ,  G06F18/22 ,  G06F18/40

CPC classification number: G16H15/00 ,  G06F3/013 ,  G06F3/167 ,  G06F18/2148 ,  G06F18/22 ,  G06F18/41 ,  G06N3/04 ,  G06N3/08 ,  G06T7/11 ,  G06V10/95 ,  G10L15/22 ,  G06T2200/24 ,  G06T2207/30041 ,  G06V2201/03

Abstract: Disclosed herein are systems, methods, and software for providing a platform for complex image data analysis using artificial intelligence and/or machine learning algorithms. One or more subsystems allow for the capturing of user input such as eye gaze and dictation for automated generation of findings. Additional features include quality metric tracking and feedback, and worklist management system and communications queueing.

公开(公告)号：US20240038391A1

公开(公告)日：2024-02-01

申请号：US18256083

申请日：2021-12-07

Applicant: NATIONAL INSTITUTE OF ADVANCED INDUSTRIAL SCIENCE AND TECHNOLOGY ,  UNIVERSITY OF TSUKUBA

Inventor： Hirokazu Nosato ,  Yuta Kochi ,  Hidenori Sakanashi ,  Masahiro Murakawa ,  Atsushi Ikeda

IPC: G16H50/20 ,  G06V10/44 ,  G06T7/00 ,  G06T7/73 ,  A61B1/05 ,  G16H30/40 ,  G06V10/764 ,  A61B1/00

CPC classification number: G16H50/20 ,  G06V10/44 ,  G06T7/0012 ,  G06T7/73 ,  A61B1/05 ,  G16H30/40 ,  G06V10/764 ,  A61B1/000096 ,  G06V2201/03 ,  G06T2207/20084 ,  G06T2207/20081 ,  G06T2207/30096 ,  G06T2207/10068

Abstract: An endoscopic diagnosis support method whereby an examined area and an unexamined area can be clearly discriminated. After a preparatory step of an observation canvas is performed in advance, a frame marking step, a key point calculation step, a preceding and following frame displacement amount calculation step, a preceding and following frame marking step are executed to thereby perform observation recording. In an image diagnosis support step IDS, support is performed such that the existence of a lesion is diagnosed in an organ on the basis of a plurality of position data marked with respect to a plurality of frames in the observation canvas data and an endoscopic image in the plural frames.

公开(公告)号：US20240037747A1

公开(公告)日：2024-02-01

申请号：US17753931

申请日：2020-09-18

Applicant: OSLO UNIVERSITETSSYKEHUS

Inventor： Sepp De RAEDT ,  Ole-Johan SKREDE ,  Håvard Emil Greger DANIELSEN ,  Tarjei Sveinsgjerd HVEEM ,  Andreas KLEPPE ,  Knut LIESTØL

IPC: G06T7/00 ,  G06V20/70 ,  G06V10/82 ,  G06V10/70 ,  G06V10/80 ,  G06V10/776 ,  G06V20/69 ,  G06V10/774 ,  A61B34/10 ,  A61B5/00 ,  G16H50/20

CPC classification number: G06T7/0016 ,  G06V20/70 ,  G06V10/82 ,  G06V10/87 ,  G06V10/80 ,  G06V10/776 ,  G06V20/695 ,  G06V10/774 ,  A61B34/10 ,  A61B5/4848 ,  G16H50/20 ,  G06V2201/03 ,  G06T2207/30024 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20021

Abstract: A computer implemented system for determining an overall-classifier for one or more source-histological-images. The system comprising: a first tile generator (204) configured to generate a plurality of first-tiles (206; 306) from the one or more source-histological-image (202; 302); and a second tile generator (205) configured to generate a plurality of second-tiles (207; 307) from the one or more source-histological-images (202; 302). The first-area of the first-tiles (206; 306) is larger than the second-area of the second-tiles (207; 307); and the second-resolution of the second-tiles (207; 307) is higher than the first-resolution of the first-tiles (206; 306). The system also includes a machine-learning network (211; 311) configured to process the plurality of first-tiles (206; 306) in order to determine a first-classifier (218; 318); a machine-learning network (215; 311) configured to process the plurality of second-tiles (207; 307) in order to determine a second-classifier (219; 319); and a classifier combiner configured to combine the first-classifier (218; 318) and the second-classifier (219; 319) to determine the overall-classifier (232; 332).

公开(公告)号：US11887724B2

公开(公告)日：2024-01-30

申请号：US17960759

申请日：2022-10-05

Applicant: NEUMORA THERAPEUTICS, INC.

Inventor： Tathagata Banerjee ,  Matthew Edward Kollada ,  Amirsina Torfi ,  Peter Crocker

IPC: G16H50/70 ,  G16H50/20 ,  G06N3/02 ,  G06V10/82 ,  G06V10/774 ,  G06N3/08 ,  G16H30/40 ,  G16H40/20 ,  G06N3/045 ,  G06V10/77 ,  G06T7/00 ,  G06N20/00

CPC classification number: G16H40/20 ,  G06N3/02 ,  G06N3/045 ,  G06N3/08 ,  G06T7/0016 ,  G06V10/774 ,  G06V10/7715 ,  G06V10/82 ,  G16H30/40 ,  G16H50/20 ,  G16H50/70 ,  G06N20/00 ,  G06T2207/10088 ,  G06T2207/10104 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016 ,  G06T2207/30104 ,  G06V2201/03

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a clinical recommendation for medical treatment of a patient. In one aspect a method comprises: receiving multi-modal data characterizing a patient, wherein the multi-modal data comprises a respective feature representation for each of a plurality of modalities; processing the multi-modal data characterizing the patient using a machine learning model, in accordance with values of a set of machine learning model parameters, to generate a patient classification that classifies the patient as being included in a patient category from a set of patient categories; determining an uncertainty measure that characterizes an uncertainty of the patient classification generated by the machine learning model; and generating a clinical recommendation for medical treatment of the patient based on: (i) the patient classification, and (ii) the uncertainty measure that characterizes the uncertainty of the patient classification.

公开(公告)号：US11885732B2

公开(公告)日：2024-01-30

申请号：US17968668

申请日：2022-10-18

Applicant: VisionGate, Inc.

Inventor： Michael G. Meyer ,  Daniel J. Sussman ,  Rahul Katdare ,  Laimonas Kelbauskas ,  Alan C. Nelson ,  Randall Mastrangelo

IPC: G06T7/00 ,  G01N15/14 ,  G06T7/194 ,  G06T7/11 ,  G16B40/20 ,  G06V20/64 ,  G06V20/69 ,  G06F18/214 ,  G06V10/40 ,  G01N15/10

CPC classification number: G01N15/1434 ,  G01N15/147 ,  G01N15/1429 ,  G01N15/1475 ,  G06F18/214 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/194 ,  G06V10/40 ,  G06V20/64 ,  G06V20/695 ,  G06V20/698 ,  G16B40/20 ,  G01N2015/1006 ,  G01N2015/1445 ,  G06T2207/10101 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06V2201/03

Abstract: A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.

公开(公告)号：US20240029870A1

公开(公告)日：2024-01-25

申请号：US18481194

申请日：2023-10-04

Applicant: FUJIFILM Corporation

Inventor： Keigo NAKAMURA

IPC: G16H30/40 ,  G16H15/00 ,  G06T7/11 ,  G06T7/00 ,  G06V10/20 ,  G06V10/82

CPC classification number: G16H30/40 ,  G16H15/00 ,  G06T7/11 ,  G06T7/0012 ,  G06V10/255 ,  G06V10/82 ,  G06V2201/03 ,  G06T2207/20084 ,  G06T2207/10072

Abstract: A document creation support apparatus comprising at least one processor, wherein the processor is configured to: extracts regions having one or more preset physical features from a medical image, and generates a comment on findings using a disease name associated with a physical feature of at least one of the extracted regions.

公开(公告)号：US20240029266A1

公开(公告)日：2024-01-25

申请号：US18356544

申请日：2023-07-21

Applicant: The Johns Hopkins University

Inventor： Janis Marie TAUBE ,  Sandor SZALAY

IPC: G06T7/11 ,  G02B21/36 ,  G06T3/40 ,  G06T5/00 ,  G06T7/00 ,  G06F18/24

CPC classification number: G06T7/11 ,  G02B21/367 ,  G06T3/4038 ,  G06T5/006 ,  G06T5/009 ,  G06T7/0014 ,  G06F18/24 ,  G06T2200/24 ,  G06T2207/10056 ,  G06T2207/20076 ,  G06T2207/20092 ,  G06T2207/30024 ,  G06V2201/03

Abstract: A device may obtain field images of a tissue sample, apply, to the field images, spatial distortion and illumination-based corrections (including corrections for photobleaching of reagents) to derive processed field images, identify, in each processed field image, a primary area including data useful for cell or subcellular component characterization, identify, in the processed field images, areas that overlap with one another, and derive information regarding a spatial mapping of cell(s) and/or sub-cellular components of the tissue sample. Deriving the information may include performing segmentation based on the data included in the primary area of each processed field image, and obtaining flux measurements based on other data included in the overlapping areas. The device may cause the information to be loaded in a data structure to enable statistical analysis of the spatial mapping for identifying factors defining normal tissue structure, associated inflammatory or neoplastic diseases and prognoses thereof, and associated therapeutics.

公开(公告)号：US11880974B2

公开(公告)日：2024-01-23

申请号：US18012585

申请日：2020-12-04

Applicant: ZHUHAI SANMED BIOTECH LTD. ,  ZHUHAI HENGQIN SANMED AITECH INC.

Inventor： Xianjun Fan ,  Xingjie Lan ,  Xin Ye ,  Yi Zhang ,  Congsheng Li

IPC: G06T7/00 ,  G06V20/70 ,  G06T7/11 ,  G06V20/69 ,  G06V10/774 ,  G06T5/20 ,  G06T5/00

CPC classification number: G06T7/0012 ,  G06T5/002 ,  G06T5/20 ,  G06T7/11 ,  G06V10/774 ,  G06V20/695 ,  G06V20/698 ,  G06V20/70 ,  G06T2207/10056 ,  G06T2207/20036 ,  G06T2207/20081 ,  G06T2207/30024 ,  G06T2207/30096 ,  G06V2201/03

Abstract: A method and device for detecting circulating abnormal cells. The method for detecting the circulating abnormal cells comprises: respectively segmenting and labelling, by using an image processing algorithm and a morphological algorithm, cell nuclei included in dark field microscope images of a plurality of probe channels (101); inputting the dark field microscope images, in which cell nuclei are labelled, of the plurality of probe channels into a pre-built circulating abnormal cell detection model to acquire the number of staining signals included in each labelled cell nucleus in the dark field microscope image of each probe channel (102); and for each labelled cell nucleus, on the basis of the number of the staining signals included in the labelled cell nucleus in the acquired dark field microscope image of each probe channel, determining whether the labelled cell nucleus belongs to a circulating abnormal cell (103). The method can effectively improve the reliability of detecting the circulating abnormal cells.